Abstract
The offshore plant equipment usually has a long life cycle. During its O&M (Operation and Maintenance) phase, since the accidental occurrence of offshore plant equipment causes catastrophic damage, it is necessary to make more efforts for managing critical offshore equipment. Nowadays, due to the emerging ICTs (Information Communication Technologies), it is possible to send health monitoring information to administrator of an offshore plant, which leads to much concern on CBM (Condition-Based Maintenance). This study introduces three approaches for predicting the next failure time of offshore plant equipment (gas compressor) with case studies, which are based on finite state continuous time Markov model, linear regression method, and their hybrid model.
Highlights
Maintenance is defined as all technical and managerial actions taken during usage period to maintain or restore the required functionality of an asset or equipment
This study introduces three approaches for predicting the failure time of offshore plant equipment with case studies, which are based on finite state continuous time Markov model, linear regression method, and their hybrid model
To cope with the limitations, this study proposes three approaches to estimate the failure time of offshore equipment based on finite state continuous time Markov model, linear regression model, and their hybrid model
Summary
Maintenance is defined as all technical and managerial actions taken during usage period to maintain or restore the required functionality of an asset or equipment. We can gather the equipment status data related to usage conditions, failure, maintenance or service events, and so on These data sets enable us to diagnose the degradation status of the equipment in a more exact way. Nowadays due to the fact that an accident of LNG FPSO in operation causes catastrophic damage, many studies have focused on a maintenance system In this vein, this study focuses on the prognostic approach for the gas compressor equipment in LNG FPSO, which is one of the main results for the Korean government supported project that is being currently developed since 2013 with the objective of implementing the predictive maintenance system for LNG FPSOs. The objective of this study is to develop the algorithm for estimating the failure time of a gas compressor based on gathered vibration sensing and failure data.
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